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A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets
Xinwei Chen1, Tao Wang1, Jia Shi1
1Key Laboratory of Thin Film and Microfabrication (Ministry of Education), Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
This study introduces a novel artificial neuron-inspired gas sensor for detecting toxic nitrogen dioxide (NO2) at room temperature. The CuS QDs/Bi2S3 NSs sensor demonstrates high sensitivity and rapid response for environmental monitoring.
Area of Science:
- Materials Science
- Nanotechnology
- Environmental Science
Background:
- Conventional gas sensors lack sufficient surface sites for gas adsorption, limiting sensitivity.
- Poor charge transport in bulk materials hinders efficient gas detection.
- Real-time toxic gas detection is crucial for public health and environmental safety.
Purpose of the Study:
- To develop a highly sensitive gas sensor for toxic nitrogen dioxide (NO2) detection.
- To leverage artificial neuron network principles for enhanced gas sensing.
- To utilize CuS quantum dots (QDs) and Bi2S3 nanosheets (NSs) for improved adsorption and charge transport.
Main Methods:
- Constructed a gas sensing structure model using CuS QDs/Bi2S3 NSs.
- Employed density functional theory (DFT) for simulation analysis.
- Fabricated and experimentally tested a neuron-like sensor for NO2 detection.
Main Results:
- CuS QDs (approx. 8 nm) exhibited high adsorbability and quantum size effect, enhancing NO2 sensitivity.
- Bi2S3 NSs acted as an efficient charge transfer network.
- The neuron-like sensor achieved a response value of 3.4, rapid response (18 s), fast recovery (338 s), low detection limit (78 ppb), and excellent NO2 selectivity.
- A wearable device enabled visual, real-time NO2 detection.
Conclusions:
- The CuS QDs/Bi2S3 NSs structure effectively enhances NO2 gas sensing performance.
- The artificial neuron-inspired design offers a promising approach for sensitive and selective toxic gas detection.
- The developed sensor technology has potential applications in wearable environmental monitoring devices.
